20.500.12556/DiRROS-22563 Applicability assessment of technologies for predictive and prescriptive analytics of nephrology big data The integration of big data into nephrology research will open new avenues for analyzing and understanding complex biological datasets, driving advances in personalized management of kidney diseases. This paper describes the multifaceted challenges and opportunities by incorporating big data in nephrology, emphasizing the importance of data standardization, advanced storage solutions, and advanced analytical methods. We discuss the role of data science workflows, including data collection, preprocessing, integration, and analysis, in facilitating comprehensive insights into disease mechanisms and patient outcomes. Furthermore, we highlight predictive and prescriptive analytics, as well as the application of large language models (LLMs) in improving clinical decision-making and enhancing the accuracy of disease predictions. The use of high-performance computing (HPC) is also examined, showcasing its role in processing large-scale datasets and accelerating machine learning algorithms. Through this exploration, we aim to provide a comprehensive overview of the current state and future directions of big data analytics in nephrology, with a focus on enhancing patient care and advancing medical research. large language models data standardization veliki jezikovni modeli standardizacija podatkov true false true Wiley ZDA Angleški jezik Slovenski jezik © 2025 The Author(s). Neznano 2025-06-04 13:43:41 2025-06-04 13:43:41 2025-10-24 03:51:38 0000-00-00 00:00:00 2025 0 0 Nasl. z nasl. zaslona; Soavtorja iz Slovenije: Ivona Vasileska, Tome Eftimov; Opis vira z dne 4. 6. 2025; str. 1-16 iss. 9/10 Vol. 25 May 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-08-18 2025-04-25 2025-05-27 004.6 1615-9861 10.1002/pmic.202400135 238301187 https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.202400135 1 2a58b30b-4139-11f0-963a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=32638 RAZ_Stojanov_Riste_2025.pdf RAZ_Stojanov_Riste_2025.pdf 1 A19EA47FD9AA8BC87BDCCFB0BF28ABFE 21948e4fa52c50c6b9393c5e799a6143a008c3521fc99793f09b5fb08e7db2b8 23e7505b-4141-11f0-963a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=32646 Institut Jožef Stefan 0 0 0